Processing facilities, such as manufacturing plants, chemical plants and oil refineries, are typically managed using process control systems. Valves, pumps, motors, heating/cooling devices, and other industrial equipment typically perform actions needed to process materials in the processing facilities. Among other functions, the process control systems often manage the use of the industrial equipment in the processing facilities.
In conventional process control systems, controllers are often used to control the operation of the industrial equipment in the processing facilities. The controllers can typically monitor the operation of the industrial equipment, provide control signals to the industrial equipment, and/or generate alarms when malfunctions are detected. Process control systems typically include one or more process controllers and input/output (I/O) devices communicatively coupled to at least one workstation and to one or more field devices, such as through analog and/or digital buses. The field devices can include sensors (e.g., temperature, pressure and flow rate sensors), as well as other passive and/or active devices. The process controllers can receive process information, such as field measurements made by the field devices, in order to implement a control routine. Control signals can then be generated and sent to the industrial equipment to control the operation of the process. Advanced controllers often use model-based control techniques to control the operation of the industrial equipment. Model-based control techniques typically involve using an empirical model to analyze input data, where the model identifies how the industrial equipment should be controlled based on the input data being received.
Model predictive controllers (MPCs) rely on dynamic models of the process, most often linear empirical models obtained by system identification. The models are used to predict the behavior of dependent variables (e.g. outputs) of a dynamic system with respect to changes in the process independent variables (e.g. inputs). In chemical processes, independent variables are most often setpoints of regulatory controllers that govern valve movement (e.g., valve positioners with or without flow, temperature or pressure controller cascades), while dependent variables are most often constraints in the process (e.g., product purity, equipment safe operating limits). The MPC uses the models and current plant measurements to calculate future moves in the independent variables that will result in operation that attempts to satisfy all independent and dependent variable constraints. The MPC then sends this set of independent variable to move to the corresponding regulatory controller setpoints to be implemented in the process.
In certain control systems, a difficulty may arise in operating different processes with characteristically different operating regimes. For example, some manufacturing processes, such as multi-variable chemical processes, may require control of both linear processes and non-linear processes simultaneously or successively for needed process control. Conventional controllers utilize a separate linear MPC and a non-linear MPC to handle each individual task.